Conventional methods for automatically diagnosing faults in products and processes utilize reasoning techniques that are difficult to develop and do not localize a fault to the extent possible or are ineffective at handling multiple faults or faults that were not previously modeled. Such automated diagnostic reasoning techniques have been incorporated in technical maintenance manuals and automatic test equipment.
Technical Maintenance Manual Background
Many technical maintenance manuals use hand coded fault trees to support the troubleshooting procedures along with technical information. Some commercially available technical maintenance manuals, however, have used dependency model reasoning and expert system rules to support troubleshooting. In each of these cases, the weakness of the diagnostic reasoning has made it secondary to the optimal sequencing of tests used to find the faulty item by elimination.
Sophisticated computer programs for generating optimum fault trees to guide the technician in troubleshooting have been developed and are commercially available from companies such as Arinc and Detex. The Arinc approach uses a dependency model of the product and generates an optimum test sequence referred to as POINTER. Pointer is a commercially available product which directs the technician through a sequence of measurements required to identify a faulty component. The Arinc system has been described in the literature by W. R. Simpson and various associates. Several key articles are referenced below:
Simpson, W. R. and H. S. Balabow. "The Arinc Research System Testability and Maintenance Program. (STAMP)". IEEE AUTOTESTCON (1982): 88-95 PA1 Simpson, W. R. "Active Testability Analysis and Interactive Fault Isolation using STAMP". IEEE AUTOTESTCON (1987):105. PA1 Simpson, W. R. "STAMP Testability and Fault Isolation Application 1981-1984". IEEE AUTOTESTCON Proceedings (1985):208-215. PA1 DePaul, R. A., Jr. "Logic Modeling as a Tool for Testability" IEEE AUTOTESTCON Proceedings (1985):203-207.
The Detex System also uses a dependency model to generate an optimum test sequence. The commercial product is called GADS (Generic Adaptive Diagnostic System). It also uses proprietary algorithms to sequence the tests. In an article by R. A. DePaul, Jr. cited below:
Both Pointer and GADS require model editors to enter data into their testability modeling programs. This has proved to be time consuming for engineers. Additionally, both methods result in the generation of fault trees that are implemented by the entry of measurement data in a specific sequence either by the technician performing the measurements or by the test program performing the same measurements. Both the modeling techniques and required measurement sequences have increased test programs cost significantly.
Concurrent with the ARINC and Detex efforts, the U.S. Government initiated a program on Integrated Diagnostics Support System (IDSS). The government program included engineers from Detex, Harris, Inc., and Giordano Associates, Inc. As a result of the IDSS program, the government developed a Weapon System Testability Analyzer (WSTA) and an Adaptive Diagnostic Subsystem (ADS) which behaved similarly to the ARINC and Detex commercial systems.
Attempts to apply the techniques inherent in the Detex, ARINC, and Navy approaches were not entirely successful. The present invention was developed to overcome the shortcomings of the prior approaches.
Automatic Test Equipment Background
Several attempts have been made to introduce the automated technical maintenance manual techniques to accomplish automated diagnostics in automatic test equipment. While the results have often been deemed successful, the test program set development community has not embraced the approach, in part, because test sequences are often driven by other test related factors making the automatic technical maintenance sequences academic.
Automated diagnostic reasoning in automatic test equipment has generally been restricted to digital tests that have been evaluated by a digital fault simulator. The primary reasoning technique has been fault signature lookup. When several faults match a simulator generated signature, probing is used to identify which part has good inputs and bad outputs. When the symptom data does not match any of the simulator generated signatures, ineffective ad hoc algorithms have been used. The complexity of modern electronics forces more and more dependence on these ad hoc methods with the consequence that some organizations no longer use fault signature lookup techniques.
Fault signature lookup methods have been commercially developed by GenRad and Teradyne among other automatic test equipment manufactures. The implementations have generally been for a specific fault simulator and a specific set of automatic test equipment.
Diagnostic Reasoning Technology Background
Commercial systems have used two diagnostic reasoning techniques based on system models: dependency analysis and fault signature lookup. Research efforts have implemented a model based minimum set covering reasoning technique and several learned data techniques using bayesian probabilities, function fitting and neural networks.
Dependency analysis is the weakest form of diagnostic reasoning. It implements fault localization by excluding all faults for which the diagnostic data contradicts the dependency model expectations by showing no failure. The resulting fault localization sets are large. As a consequence, dependency analysis is seldom used as a reasoning technique by itself. Instead, it is used as part of a diagnostic test sequence which eventually tests many of the inputs and outputs of the faulty part.
For the failures that have been modeled, the fault signature lookup technique has the virtue missing in the dependency analysis. It provides excellent fault localization. With much less data, the fault signature lookup technique can identify the faulty part or a small set of alternative faulty parts where dependency analysis would suspect a prohibitive number of possible faulty parts. The weakness of the fault signature lookup technique is in its inability to handle either multiple faults or faults that have not been modeled. In systems where fault signature lookup has been implemented, ad hoc algorithms have been added to address such situations. These techniques have generally not proved successful.
The minimum fault covering reasoning technique has the same fault localization capabilities as fault signature lookup plus the ability to handle multiple faults and faults that have not been modeled. Its outputs are minimum covering combinations of faults that would cause the model to display the same diagnostic results as are observed. The identification of these combinations is made efficient through the use of generator sets made up of faults that can be substituted indistinguishably in listing a fault set that shows the observed symptoms. The weakness of this technique lies in the amount of time, even for a supercomputer, that it takes to generate this list.
Other diagnostic approaches which exist today include "rule based" expert systems generated with artificial intelligence computer software languages such as LISP. Expert systems thus generated suffer from the problem of expensive generation and the requirement for having system experts available to generate rules. They also are difficult to change in the field when problems arise that had not been anticipated. Extensive literature exists on rule based expert systems across many different industrial areas.
Neural networks have received a great deal of attention from industry in order to solve the diagnostic problem that exists. Neural networks will learn the behavior of a system and duplicate that behavior in the field. Neural networks are trained by introducing many failures and providing the neural network with test results. This training process has caused some problems on implementation of neural networks.
The above conventional methods and the existing technologies all require expensive, labor intensive approaches for generating the knowledge base to be used for diagnostics. They also produce fault trees which must be sequenced by expensive automatic test programs or technician based manual test procedures.
Giordano Automation has developed and productized a "Diagnostician" which uses a model based reasoning technique which is stronger than the dependency analysis and fault signature lookup techniques and is less computer intensive than the minimum set covering technique.